{"id":"https://openalex.org/W4404351499","doi":"https://doi.org/10.1145/3677052.3698632","title":"Quantile Regression using Random Forest Proximities","display_name":"Quantile Regression using Random Forest Proximities","publication_year":2024,"publication_date":"2024-11-14","ids":{"openalex":"https://openalex.org/W4404351499","doi":"https://doi.org/10.1145/3677052.3698632"},"language":"en","primary_location":{"id":"doi:10.1145/3677052.3698632","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698632","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698632","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698632","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091468980","display_name":"Mingshu Li","orcid":"https://orcid.org/0000-0002-5129-6097"},"institutions":[{"id":"https://openalex.org/I55884533","display_name":"BlackRock (United States)","ror":"https://ror.org/031dc4703","country_code":"US","type":"company","lineage":["https://openalex.org/I55884533"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Mingshu Li","raw_affiliation_strings":["BlackRock, Inc., United States"],"raw_orcid":"https://orcid.org/0000-0002-5129-6097","affiliations":[{"raw_affiliation_string":"BlackRock, Inc., United States","institution_ids":["https://openalex.org/I55884533"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040045971","display_name":"Bhaskarjit Sarmah","orcid":"https://orcid.org/0009-0004-3076-9539"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bhaskarjit Sarmah","raw_affiliation_strings":["BlackRock, Inc., India"],"raw_orcid":"https://orcid.org/0009-0004-3076-9539","affiliations":[{"raw_affiliation_string":"BlackRock, Inc., India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008885741","display_name":"Dhruv Desai","orcid":"https://orcid.org/0009-0006-7728-0081"},"institutions":[{"id":"https://openalex.org/I55884533","display_name":"BlackRock (United States)","ror":"https://ror.org/031dc4703","country_code":"US","type":"company","lineage":["https://openalex.org/I55884533"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dhruv Desai","raw_affiliation_strings":["BlackRock, Inc., United States"],"raw_orcid":"https://orcid.org/0009-0006-7728-0081","affiliations":[{"raw_affiliation_string":"BlackRock, Inc., United States","institution_ids":["https://openalex.org/I55884533"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025106746","display_name":"Joshua Rosaler","orcid":"https://orcid.org/0000-0002-5824-5671"},"institutions":[{"id":"https://openalex.org/I55884533","display_name":"BlackRock (United States)","ror":"https://ror.org/031dc4703","country_code":"US","type":"company","lineage":["https://openalex.org/I55884533"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joshua Rosaler","raw_affiliation_strings":["BlackRock, Inc., United States"],"raw_orcid":"https://orcid.org/0000-0002-5824-5671","affiliations":[{"raw_affiliation_string":"BlackRock, Inc., United States","institution_ids":["https://openalex.org/I55884533"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066625094","display_name":"Snigdha Bhagat","orcid":"https://orcid.org/0000-0002-0310-7925"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Snigdha Bhagat","raw_affiliation_strings":["BlackRock, Inc., India"],"raw_orcid":"https://orcid.org/0000-0002-0310-7925","affiliations":[{"raw_affiliation_string":"BlackRock, Inc., India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5027090237","display_name":"Philip Sommer","orcid":null},"institutions":[{"id":"https://openalex.org/I55884533","display_name":"BlackRock (United States)","ror":"https://ror.org/031dc4703","country_code":"US","type":"company","lineage":["https://openalex.org/I55884533"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Philip Sommer","raw_affiliation_strings":["BlackRock, Inc., United States"],"raw_orcid":"https://orcid.org/0009-0008-9550-0388","affiliations":[{"raw_affiliation_string":"BlackRock, Inc., United States","institution_ids":["https://openalex.org/I55884533"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056701307","display_name":"Dhagash Mehta","orcid":"https://orcid.org/0000-0002-1040-9032"},"institutions":[{"id":"https://openalex.org/I55884533","display_name":"BlackRock (United States)","ror":"https://ror.org/031dc4703","country_code":"US","type":"company","lineage":["https://openalex.org/I55884533"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dhagash Mehta","raw_affiliation_strings":["BlackRock, Inc., United States"],"raw_orcid":"https://orcid.org/0000-0002-1040-9032","affiliations":[{"raw_affiliation_string":"BlackRock, Inc., United States","institution_ids":["https://openalex.org/I55884533"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5091468980"],"corresponding_institution_ids":["https://openalex.org/I55884533"],"apc_list":null,"apc_paid":null,"fwci":0.9934,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.8110078,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"728","last_page":"736"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.977400004863739,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10320","display_name":"Neural Networks and Applications","score":0.977400004863739,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.965499997138977,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9405999779701233,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6281093955039978},{"id":"https://openalex.org/keywords/quantile-regression","display_name":"Quantile regression","score":0.6235978007316589},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.45697009563446045},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.42398083209991455},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2559243440628052},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.17625674605369568}],"concepts":[{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6281093955039978},{"id":"https://openalex.org/C63817138","wikidata":"https://www.wikidata.org/wiki/Q3455889","display_name":"Quantile regression","level":2,"score":0.6235978007316589},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.45697009563446045},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.42398083209991455},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2559243440628052},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.17625674605369568}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3677052.3698632","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698632","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698632","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Conference on AI in Finance","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3677052.3698632","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3677052.3698632","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3677052.3698632","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 5th ACM International Conference on AI in Finance","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4404351499.pdf","grobid_xml":"https://content.openalex.org/works/W4404351499.grobid-xml"},"referenced_works_count":27,"referenced_works":["https://openalex.org/W1996742192","https://openalex.org/W2047081748","https://openalex.org/W2207674461","https://openalex.org/W2618521746","https://openalex.org/W2775379762","https://openalex.org/W2787894218","https://openalex.org/W2798413829","https://openalex.org/W2807414627","https://openalex.org/W2807541381","https://openalex.org/W2948167064","https://openalex.org/W3004893834","https://openalex.org/W3016597555","https://openalex.org/W3022643593","https://openalex.org/W3024247346","https://openalex.org/W3033700965","https://openalex.org/W3037706619","https://openalex.org/W3099697354","https://openalex.org/W3110616649","https://openalex.org/W3175934715","https://openalex.org/W4205545960","https://openalex.org/W4297574015","https://openalex.org/W4304808951","https://openalex.org/W4306935468","https://openalex.org/W4361982189","https://openalex.org/W4391288585","https://openalex.org/W6610017368","https://openalex.org/W6776486363"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W1924178503","https://openalex.org/W3135126032","https://openalex.org/W2390279801"],"abstract_inverted_index":{"Due":[0],"to":[1,35,104,177,198],"the":[2,19,31,36,76,81,91,113,120,124,128,133,145,149,178,187],"dynamic":[3],"nature":[4,38],"of":[5,39,80,94,127,147,153,181],"financial":[6],"markets,":[7],"maintaining":[8],"models":[9,68],"that":[10,65,111,158,186],"produce":[11],"precise":[12],"predictions":[13],"over":[14],"time":[15],"is":[16,190],"difficult.":[17],"Often":[18],"goal":[20],"isn\u2019t":[21],"just":[22],"point":[23],"prediction":[24,175],"but":[25],"determining":[26],"uncertainty.":[27],"Quantifying":[28],"uncertainty,":[29],"especially":[30],"aleatoric":[32],"uncertainty":[33],"due":[34],"unpredictable":[37],"market":[40],"drivers,":[41],"helps":[42],"investors":[43],"understand":[44],"varying":[45],"risk":[46],"levels.":[47],"Recently,":[48],"quantile":[49,62,72,106,160,199],"regression":[50,63,73,161],"forests":[51,74,110],"(QRF)":[52],"have":[53],"emerged":[54],"as":[55],"a":[56,85,95,101],"promising":[57],"solution:":[58],"Unlike":[59],"most":[60],"basic":[61],"methods":[64],"need":[66],"separate":[67],"for":[69],"each":[70],"quantile,":[71],"estimate":[75],"entire":[77],"conditional":[78,125,171],"distribution":[79,126],"target":[82,129,172],"variable":[83],"with":[84],"single":[86],"model,":[87],"while":[88],"retaining":[89],"all":[90],"salient":[92],"features":[93],"typical":[96],"random":[97,109],"forest.":[98],"We":[99,131,156,183],"introduce":[100],"novel":[102],"approach":[103],"compute":[105],"regressions":[107],"from":[108],"leverages":[112],"proximity":[114],"(i.e.,":[115],"distance":[116],"metric)":[117],"learned":[118],"by":[119],"model":[121],"and":[122,140,174],"infers":[123],"variable.":[130],"evaluate":[132],"proposed":[134,188],"methodology":[135],"using":[136,159,162],"publicly":[137],"available":[138],"datasets":[139],"then":[141],"apply":[142],"it":[143],"towards":[144],"problem":[146],"forecasting":[148],"average":[150],"daily":[151],"volume":[152],"corporate":[154],"bonds.":[155],"show":[157],"Random":[163],"Forest":[164],"proximities":[165],"demonstrates":[166],"superior":[167],"performance":[168],"in":[169],"approximating":[170],"distributions":[173],"intervals":[176],"original":[179],"version":[180],"QRF.":[182],"also":[184],"demonstrate":[185],"framework":[189],"significantly":[191],"more":[192],"computationally":[193],"efficient":[194],"than":[195],"traditional":[196],"approaches":[197],"regressions.":[200]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}